Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation.
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%0 Conference Paper
%1 conf/cib/BroussardI09
%A Broussard, Randy P.
%A Ives, Robert W.
%B CIB
%D 2009
%I IEEE
%K dblp
%P 46-51
%T Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation.
%U http://dblp.uni-trier.de/db/conf/cib/cib2009.html#BroussardI09
%@ 978-1-4244-2773-4
@inproceedings{conf/cib/BroussardI09,
added-at = {2017-05-19T00:00:00.000+0200},
author = {Broussard, Randy P. and Ives, Robert W.},
biburl = {https://www.bibsonomy.org/bibtex/295ec706cbd5cc849264ed34b06ff93d6/dblp},
booktitle = {CIB},
crossref = {conf/cib/2009},
ee = {https://doi.org/10.1109/CIB.2009.4925685},
interhash = {528c45d080148736454c4b671af09150},
intrahash = {95ec706cbd5cc849264ed34b06ff93d6},
isbn = {978-1-4244-2773-4},
keywords = {dblp},
pages = {46-51},
publisher = {IEEE},
timestamp = {2019-10-17T22:19:00.000+0200},
title = {Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation.},
url = {http://dblp.uni-trier.de/db/conf/cib/cib2009.html#BroussardI09},
year = 2009
}
